An MPC-based Energy Management System for multiple residential microgrids

Parisio, Alessandra; Wiezorek, Christian; Kyntäjä, Timo; Elo, Joonas; Johansson, Karl Henrik

doi:10.1109/coase.2015.7294033

Cited by 36 publications

(23 citation statements)

References 25 publications

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“…However, so far, MPC was also successfully applied to several other applications related to building controls, but those studies will not be discussed in the present work. For example, the MPC formulation can be used to control and optimize residential appliances [11][12][13], the building interaction with a smart grid or micro-grid [14][15][16][17][18][19], or an on-site renewable energy generation system. More broadly, MPC algorithms found successful application in all of those implementations that require management of energy resources for building prosumers under designated constraints and dynamic energy pricing models [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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Section: Introductionmentioning

confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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“…The modeling and MPC problem formulation are detailed below [22]. This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: Modeling Of Der and Flexible Loadsmentioning

confidence: 99%

“…with I E,PV (k) giving the received area-specific solar The modeling of the component adopts a data-driven approach, where data from a real-world deployment were used [22]. The micro-CHP model is:…”

Section: Photovoltaic Systemmentioning

confidence: 99%

Multi‐location virtual smart grid laboratory with testbed for analysis of secure communication and remote co‐simulation: concept and application to integration of Berlin, Stockholm, Helsinki

Wiezorek

Parisio

Kyntäjä

et al. 2017

IET Generation, Transmission & Distribution

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The process of advancement and validation of smart grid technologies and systems calls for the availability of diverse expertise and resources. In response to this consideration, the Virtual Smart Grid Lab (VSGL) was developed as described in this paper. At the core of the VSGL is a novel communication platform for seamlessly connecting geographically distributed laboratories with distinct competences. The platform has the dual purpose of opening access to resources of remote partner laboratory sites and offering the capability to emulate, analyze, and test smart grid communication networks involved in linking the distributed laboratory resources. The VSGL implementation is validated through a use case, in which the resources of R&D laboratories in three European countries are connected to form an aggregated system of distributed energy resources. The operation of the latter was coordinated through an energy management system based on model predictive control (MPC). The VSGL was found to be very suitable to meet the communication-specific requirements of such type of study. In addition, for this particular case the effectiveness of the MPC subject to diverse implementations of communication links was substantiated.

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“…In [9], a novel mixed integer linear approach embedded within an MPC framework is proposed to optimize microgrid operations efficiently while satisfying time-varing request and operation constraints. [10], [11] present an MPC-based EMS for multiple microgrids to optimally manage and coordinate energy supply and demand, which aims at minimizing the overall costs of each microgrid. Especially, a power flow management method for a network of cooperating microgrids is proposed by formulating the problem in a centralized MPC framework and the supply and demand is balanced with maximization of Renewable Energy Source (RES) [12].…”

Section: Introductionmentioning

confidence: 99%

Distributed MPC for Coordinated Energy Efficiency Utilization in Microgrid Systems

et al. 2019

IEEE Trans. Smart Grid

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To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal Distributed Model Predictive Control (DMPC) strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems and some controllable loads, can maintain its own system-wide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply-demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.

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An MPC-based Energy Management System for multiple residential microgrids

Cited by 36 publications

References 25 publications

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Multi‐location virtual smart grid laboratory with testbed for analysis of secure communication and remote co‐simulation: concept and application to integration of Berlin, Stockholm, Helsinki

Distributed MPC for Coordinated Energy Efficiency Utilization in Microgrid Systems

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